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Multiclass classification of nutrients deficiency of apple using deep neural network

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Abstract

Agriculture industry is the foundation of Indian economy where quality fruit production plays an important role. Apple or pome fruits are always in demand because of rich nutrients in it. Hence, to analyze and recognize the nutrients deficiency in fruits, a deep neural-based model is being proposed. This model automatically classifies and recognizes the type of deficiency present in apple. In this paper, a database has been created for four major types of nutrients deficiency in apples and used for training and validation of the proposed deep convolutional network. The model is tuned with k-fold cross-validation. The hyper-parameters such as epoch are set at 100 and batch size kept at 5. Finally, the model is tested with the testing data and achieved an average accuracy of 98.24% with k-fold cross-validation set to 15. The model accuracy depends on the hyper-parameters. The process of features optimization reduces the risk of overfitting of the model. Hence, careful selection of hyper-parameters is important for the convergence of cost function to the global minima that results in minimum misclassification.

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References

  1. Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378–384

    Article  Google Scholar 

  2. Krizhevsky A, Sutskever I, Geoffrey HE (2012) ImageNet classification with deep convolutional neural networks, Adv Neural Inf Proces Syst 25 NIPS2012, arXiv:1102.0183:1–9

  3. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks arXiv:1311.2901v3 cs.CV, Computer Vision ECCV 2014 8689, arXiv:1311.2901: 818–833

  4. Simonyan K, Zisserman A (2014) Very deep convolutional networks for largescale image recognition, pp 1–14

  5. Kawasaki R, Uga H, Kagiwada S, Iyatomi H (2015) Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. In: Proceedings of the international symposium on visual computing (ISVC), Las Vegas, NV, USA, pp 638–645

  6. Kumar Y, Dubey AK, Ratan R et al (2019) Computer vision based analysis and detection of defects in fruits causes due to nutrients deficiency. Cluster Comput. https://doi.org/10.1007/s10586-019-03029-6

    Article  Google Scholar 

  7. Jiang B et al (2019) Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues. Artific Intell Agric 1:1–8

    Google Scholar 

  8. Sambasivam G et al (2020) A predictive machine learning application in agriculture: cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egypt Inform J. Available online 9 March 2020, Corrected Proof (in Press)

  9. Lopez JJ, Cobos M, Aguilera E (2011) Computer-based detection and classification of flaws in citrus fruits. Neural Comput Appl 20:975–981

    Article  Google Scholar 

  10. Somayeh Mousavi B, Soleymani F, Razmjooy N (2013) Color image segmentation using neuro-fuzzy system in a novel optimized color space. Neural Comput Appl 23:1513–1520

    Article  Google Scholar 

  11. Kumar Y, Dubey AK, Ratan R (2019) Development of feature based classification of fruit using deep learning. Int J Innov Technol Explor Eng. 8(12):3285–3290. https://doi.org/10.35940/ijitee.L2804.1081219

    Article  Google Scholar 

  12. Kumar Y, Dubey AK, Ratan R (2019) Automatic detection and classification of nutrients deficiency in fruit based on automated machine learning. Int J Eng Adv Technol. 9(1):1901–1909. https://doi.org/10.35940/ijeat.A1029.109119

    Article  Google Scholar 

  13. Makkar T, Verma S, Kumar Y, Dubey AK (2018) Analysis and detection of fruit defect using neural network. In: Panda B, Sharma S, Roy N (eds) Data science and analytics. REDSET 2017. Communications in computer and information science, vol 799. Springer, Singapore, pp. 554–567. https://doi.org/10.1007/978-981-10-8527-7_46

    Chapter  Google Scholar 

  14. Lin S (2013) Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network. Neural Comput Appl 22:783–791

    Article  Google Scholar 

  15. Hanson E, Apple nutrition, diagnosing and avoiding nutrient deficiencies. https://www.canr.msu.edu/uploads/files/Applenutrition-EricHanson.pdf. Accessed on 14 July 2020

  16. Convolutional Neural Network, https://medium.com/@phidaouss/convolutional-neural-networks-cnn-or-convnets-d7c688b0a207, Accessed on: 28 June 2020

  17. Brownlee J, Machine Learning Mastery. https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/. Accessed on: 28 June 2020

  18. Arakeria MP, Lakshmana MS (2016) Computer vision based fruit grading system for quality evaluation of tomato in agriculture industry. Int Conf Commun Comput Virtual 79:426–433

    Google Scholar 

  19. Khoje S, Bodhe S (2013) Comparative performance evaluation of size metrics and classifiers in computer vision based automatic mango grading. Int J Comput Appl 61(9):1–7

    Google Scholar 

  20. Zhang Y, Wu L (2012) Classification of fruits using computer vision and a multiclass support vector machine. Sensors 12(9):12489–12505

    Article  Google Scholar 

  21. Cavallo DP et al (2018) Non-destructive and contactless quality evaluation of table grapes by a computer vision system. Comput Electron Agric 156:558–564

    Article  Google Scholar 

  22. Agilandeeswari L et al (2017) Automatic grading system for mangoes using multiclass SVM classifier. Int J Pure Appl Math 116(23):515–523

    Google Scholar 

Download references

Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors gratefully acknowledge for the laboratory support provided by Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, UP, India.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India

    Yogesh Kumar & Ashwani Kumar Dubey

  2. Department of Electronics and Communication Engineering, MVN University, Palwal, Haryana, India

    Rajeev Ratan Arora

  3. ISEG, University of Lisbon, Rua do Quelhas, Nº 6, 1200-781, Lisbon, Portugal

    Alvaro Rocha

Authors
  1. Yogesh Kumar
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  2. Ashwani Kumar Dubey
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  3. Rajeev Ratan Arora
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  4. Alvaro Rocha
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Contributions

Yogesh contributed in conceptualization, methodology, software, data curation, validation, writing—original draft preparation. Ashwani Kumar Dubey contributed to conceptualization, methodology, validation, supervision, reviewing and editing. Rajeev Ratan contributed to supervision, reviewing and editing. Alvaro Rocha carried out validation, reviewing and editing.

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Correspondence to Ashwani Kumar Dubey.

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Kumar, Y., Dubey, A.K., Arora, R.R. et al. Multiclass classification of nutrients deficiency of apple using deep neural network. Neural Comput & Applic 34, 8411–8422 (2022). https://doi.org/10.1007/s00521-020-05310-x

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  • DOI: https://doi.org/10.1007/s00521-020-05310-x

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